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cafferychen777

ChatSpatial

by cafferychen777
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
load_dataA

Load spatial transcriptomics data with comprehensive metadata profile.

Args:
    data_path: Path to data file or directory
    data_type: 'visium', 'xenium', 'slide_seq', 'merfish', 'seqfish', or 'generic'
    name: Optional dataset name

Returns:
    SpatialDataset with cell/gene counts and metadata profiles
preprocess_dataA

Run QC, filtering, normalization, and highly variable gene selection.

This tool does not compute PCA, UMAP, clustering, or neighbor graphs. Run
compute_embeddings() afterward when downstream tools require those artifacts.

Args:
    data_id: Dataset ID
    params: Preprocessing parameters (all have sensible defaults)
compute_embeddingsB

Compute dimensionality reduction (PCA, UMAP), clustering, and neighbor graphs.

Args:
    data_id: Dataset ID
    params: Embedding parameters (PCA, UMAP, clustering, etc.)
visualize_dataB

Visualize spatial transcriptomics data. Set plot_type and subtype in params; see VisualizationParameters schema for all options.

Args:
    data_id: Dataset ID
    params: Visualization parameters (plot_type, subtype, genes, output_format, dpi, etc.)
annotate_cell_typesB

Annotate cell types in spatial transcriptomics data.

Args:
    data_id: Dataset ID
    params: Annotation parameters (method, reference_data_id, cell_type_key, etc.)

Note: Reference methods (tangram, scanvi) require reference_data_id to be preprocessed first.
analyze_spatial_statisticsC

Analyze spatial statistics and autocorrelation patterns.

Args:
    data_id: Dataset ID
    params: Analysis parameters (analysis_type, cluster_key, genes). See SpatialStatisticsParameters for all types.
find_markersC

Find differentially expressed genes between groups.

Args:
    data_id: Dataset ID
    params: Required - group_key and optional method, group1/group2, n_top_genes, etc.
compare_conditionsA

Compare experimental conditions using pseudobulk differential expression (DESeq2).

Args:
    data_id: Dataset ID
    params: Required - condition_key, condition1, condition2, sample_key, etc.
analyze_cnvA

Analyze copy number variations (CNVs) in spatial transcriptomics data.

Args:
    data_id: Dataset identifier
    params: Required - reference_key, reference_categories, and optional method/thresholds.
analyze_velocity_dataB

Analyze RNA velocity to understand cellular dynamics. Requires 'spliced' and 'unspliced' layers.

Args:
    data_id: Dataset ID
    params: Velocity parameters (method, scvelo_mode, etc.)
analyze_trajectory_dataC

Infer cellular trajectories and pseudotime ordering.

Args:
    data_id: Dataset ID
    params: Trajectory parameters (method, root_cell, spatial_weight, etc.)
integrate_samplesB

Integrate multiple spatial transcriptomics samples into a unified dataset.

Args:
    data_ids: List of dataset IDs to integrate
    params: Integration parameters (method, batch_key, n_pcs, etc.)
deconvolve_dataA

Deconvolve spatial spots to estimate cell type proportions.

Args:
    data_id: Dataset ID
    params: Required - method, cell_type_key, reference_data_id. See DeconvolutionParameters for all methods and options.
identify_spatial_domainsC

Identify spatial domains and tissue architecture.

Args:
    data_id: Dataset ID
    params: Spatial domain parameters (method, n_domains, resolution, etc.)
analyze_cell_communicationC

Analyze cell-cell communication and ligand-receptor interaction patterns.

Args:
    data_id: Dataset ID
    params: Required - species, cell_type_key, and method. For mouse with liana, set liana_resource='mouseconsensus'.
analyze_enrichmentC

Perform gene set enrichment analysis.

Args:
    data_id: Dataset ID
    params: Required - species must be specified. See EnrichmentParameters for methods and gene_set_database options.
find_spatial_genesC

Identify spatially variable genes.

Args:
    data_id: Dataset ID
    params: Spatial variable gene parameters (method, n_top_genes, etc.)
register_spatial_dataC

Register/align spatial transcriptomics data across sections

Args:
    source_id: Source dataset ID
    target_id: Target dataset ID to align to
    params: Registration parameters (method, alignment settings, etc.)

Returns:
    Registration result with method, dataset IDs, spot counts, and registered spatial key
export_dataA

Export dataset to disk for external script access.

Args:
    data_id: Dataset ID to export
    path: Custom path (default: ~/.chatspatial/active/{data_id}.h5ad)

Returns:
    Absolute path where data was exported
reload_dataA

Reload dataset from disk after external script modifications.

Args:
    data_id: Dataset ID to reload (must exist in MCP memory)
    path: Custom path (default: ~/.chatspatial/active/{data_id}.h5ad)

Returns:
    Summary of reloaded dataset

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored across 20 tools

Disambiguation4/5

Tools map to distinct analytical tasks such as loading, preprocessing, embedding, annotation, and downstream analyses, with descriptions that clarify each tool's role. However, 'find_markers' and 'compare_conditions' both involve differential expression and could be confused, and 'analyze_spatial_statistics' overlaps somewhat with 'find_spatial_genes'.

Naming Consistency5/5

Nearly all tools follow a consistent verb_noun pattern like load_data, visualize_data, and analyze_enrichment. The naming is predictable and uniform, making tool selection straightforward.

Tool Count3/5

20 tools is on the heavy side and sits in the 16-25 range that feels bloated for a typical MCP server. Each tool does appear to serve a distinct stage in the spatial transcriptomics workflow, but the overall surface is large.

Completeness4/5

The set covers the full analysis lifecycle from data loading, preprocessing, embedding, annotation, and a wide range of downstream analyses through export/reload. Minor gaps include no list/delete data management tools and no explicit metadata retrieval tool after loading, but these are workable.

Maintenance

ActivityMaintained
ResponsivenessResponsive

Resources